Excess Mortality Versus COVID‐19 Death Rates: A Spatial Analysis of Socioeconomic Disparities and Political Allegiance Across U.S. States

• Published date: 01 June 2022
• Author: Janine Aron,John Muellbauer
• Date: 01 June 2022
• DOI: http://doi.org/10.1111/roiw.12570

© 2022 International Association for Research in Income and Wealth

348

EXCESS MORTALITY VERSUS COVID- 19 DEATH RATES: A SPATIAL

ANALYSIS OF SOCIOECONOMIC DISPARITIES AND POLITICAL

ALLEGIANCE ACROSS U.S. STATES

by Janine aron

Department of Economics, University of Oxford, Institute for New Economic Thinking, Oxford Martin

School, University of Oxford and the Leverhulme Centre for Demographic Science, Nufeld College

AND

John Muellbauer*

Nufeld College, Institute for New Economic Thinking, Oxford Martin School, Leverhulme Centre for

Demographic Science, University of Oxford

Excess mortality is a more robust measure than the counts of COVID- 19 deaths typically used in

epidemiological and spatial studies. Measurement issues around excess mortality, considering data

quality and comparability both internationally and within the U.S., are surveyed. This paper is the

first state- level spatial analysis of cumulative excess mortality for the U.S. in the first full year of the

pandemic. There is strong evidence that, given appropriate controls, states with higher Democrat vote

shares experienced lower excess mortality (consistent with county- level studies of COVID- 19 deaths).

Important demographic and socio- economic controls from a broad set tested were racial composition,

age structure, population density, poverty, income, temperature, and timing of arrival of the pandemic.

Interaction effects suggest the Democrat vote share effect of reducing mortality was even greater in

states where the pandemic arrived early. Omitting political allegiance leads to a significant underestima-

tion of the mortality disparities for minority populations.

JEL Codes: I14, I18, J11

Keywords: excess mortality, COVID- 19, spatial analysis, U.S. states, political polarization

1. introduction

Excess mortality is a count of deaths from “all causes” expressed relative to

the benchmark of “normal” deaths. “Normal” death rates reflect persistent factors

such as the age composition of the population, the incidence of smoking and air

pollution, the prevalence of obesity, poverty and inequality, and the normal qual-

ity of health service delivery. Normal deaths are typically estimated from several

years of data on pre- pandemic mortality using methods of varying sophistication.

Note: We are grateful to Eric Beinhocker and David Hendry of INET for comments, to Lauren

Rossen of the U.S. CDC (Centers for Disease Control and Prevention), and to participants at seminar

meetings at the Dallas Federal Reserve, Peterson Institute, DC, particularly Olivier Blanchard and

Marcus Noland, and at INET, Oxford University, for discussions.

*Correspondence to: John Muellbauer, Nuffield College, University of Oxford, New Road,

Oxford, OX1 1NF, UK (john.muellbauer@nuffield.ox.ac.uk).

Review of Income and Wealth

Series 68, Number 2, June 2022

DOI: 10.1111/roiw.12570

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Review of Income and Wealth, Series 68, Number 2, June 2022

349

© 2022 International Association for Research in Income and Wealth

In a pandemic, deaths rise sharply, but causes are often inaccurately recorded, par-

ticularly when reliable tests are not widely available. Thus, the death counts1

attributed to COVID- 19 may have been significantly understated. Excess mortality

data overcome two problems in reporting COVID- 19- related deaths. Miscounting

from the misdiagnosis or under- reporting of COVID- 19- related deaths is avoided.

Excess mortality data also include “collateral damage” from other health condi-

tions, left untreated if the health system is overwhelmed by COVID- 19 cases, or

from deliberate actions that prioritize patients with COVID- 19 over those with

other symptoms. Precautionary measures taken by governments and individuals

may also influence death rates in a pandemic. Deaths from traffic accidents and

deaths from other infectious diseases such as influenza may decline; however, sui-

cide rates may rise.2 Excess mortality captures the net outcome of all these factors.

Excess mortality data can be used to draw lessons from cross- country and

within- country differences and to analyze the social and economic consequences

of the pandemic and of lockdown restrictions. Excess death figures may help to

avoid the measurement biases inherent in other data typically used to estimate the

virus reproduction rate, R, in epidemiological models,3 crucial for designing and

assessing non- pharmaceutical interventions such as lock- downs.

Studies comparing the U.S. to other countries find that in 2020 it ranked

amongst the highest in COVID- 19 deaths per 100,000 (Bilinski, 2020) and in rates

of excess deaths (OECD paper by Morgan et al. (2020), ONS (2021a) and earlier

versions, and Aron and Muellbauer (2020c)). Woolf et al. (2020, 2021), comparing

U.S. mortality from COVID- 19 (March– October, 2020) to leading causes of death

two years before the pandemic (March– October, 2018), find that COVID- 19 was

one of the leading causes of death; in the Spring and late Autumn of that year, it

was the leading cause of death in the U.S. The pandemic is likely to exacerbate the

decline in life expectancy that has been apparent since 2014 (Koh et al., 2020).

Virtually all spatial analyses of mortality in the U.S. are based on counts of

COVID- 19 deaths. The only exception is a county- level study of excess mortality by

Stokes et al. (2021), of which more below. Davies et al. (2020) is an excellent spatial

study of excess mortality in England. An indication of the limitations and biases in

the data on infections and COVID- 19 deaths is given in IHME (2021), who suggest

that death counts are a less biased estimate of true COVID- related deaths than

COVID- 19 case counts are of the true number of infections.4 Yet, Weinberger et al.

(2020) find that official tallies likely undercount U.S. deaths due to the virus, with the

completeness of the tallies varying markedly between states; they also advocate excess

all- cause mortality data as more reliable to estimate the full COVID- 19 burden.

1For example, see webpage: COVID- 19 Dashboard by the Center for Systems Science and

Engineering (CSSE), Johns Hopkins University (JHU). https://www.arcgis.com/apps/opsda shboa rd/

index.html#/bda75 94740 fd402 99423 467b4 8e9ecf6

2Other examples are increases in self- harm, domestic abuse and other crime; use of tobacco, drugs

and alcohol; and anxiety and changed quality of diet from loss of jobs and income, see Kontis et al.

(2020).

3See the evidence of Prof. John Edmunds to the UK Science and Technology Parliamentary Select

Committee on 7 May 2020. He explained that while excess mortality data lag COVID- 19 infections, the

data are an important check on earlier estimates of the rate of spread of the virus.

4Case count data are affected by differences in treatment- seeking behavior, testing protocols and

access to care, and further compromised by infectious asymptomatic individuals or pre- symptomatic

individuals. Testing results may be compromised by accuracy concerns.

Review of Income and Wealth, Series 68, Number 2, June 2022

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© 2022 International Association for Research in Income and Wealth

In the first 52 weeks of the pandemic, there were around 650,300 excess

deaths in the U.S., compared with COVID- 19 deaths of around 499,500, sourced

from Coronavirus Resource Center, Johns Hopkins University (JHU), or around

530,000, when sourced from the U.S. Centers for Disease Control and Prevention

(CDC). Figure 1 shows the time profile of weekly per capita excess deaths at the

national level, and the ratio of the CDC count of COVID- 19 deaths to excess

deaths. This shows severe under- counting of COVID- 19 deaths at the start of the

pandemic in the Spring and suggests considerable under- counting in the Summer

and early Autumn of 2020. The figure also shows the ratio of JHU- sourced

COVID- 19 deaths to CDC- sourced COVID- 19 deaths; the high ratio suggests

an even greater under- counting by the JHU source than the CDC source at the

start of the pandemic. Moreover, the divergence between the two measures persists

throughout the pandemic and is greatest at the peaks of the waves. Our empirical

work on COVID- 19 deaths suggests strongly that the CDC- sourced COVID- 19

death count is preferable to the JHU data, see Section5.4. Figure 2 ranks the U.S.

states by the cumulated excess deaths per capita for the 52weeks, comparing with

the P- score, measuring the ratio of excess deaths to normal deaths, see Table 1,

and the CDC measure of per capita COVID- 19 deaths. Comparing the COVID- 19

death count to excess deaths across states reveals considerable variations in the

degree of under- counting.

Figure 1. Weekly U.S. Per Capita Excess Deaths, the Ratio of CDC- sourced to JHU- sourced

COVID- 19 Deaths, and the Ratio of CDC- recorded COVID- 19 Deaths to Excess Deaths

Notes: Calculations by the authors using data from the U.S. Centers for Disease Control and

Prevention (CDC) and the Coronavirus Resource Center, Johns Hopkins University, see Tables1 and 2.

Weekly per capita excess deaths are expressed as per 100,000 persons. [Colour figure can be viewed at

wileyonlinelibrary.com]

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